0 JOURNAL OF COPUER, VOL. 6, NO. 6, JUNE 0 Enterprse Knowledge anagement Applcaton Evaluaton based on Cloud Gravty Center odel and Fuzzy Extended AHP Jngl Zheng chool of Economcs and Busness Admnstraton, Chongqng Unversty, Chongqng, Chna chool of Economcs and anagement, Chongqng Normal Unversty, Chongqng, Chna Emal: lh-lly@63.com Abstract Enterprse knowledge management has mportant nfluence on an enterprse core competency, and enterprse knowledge management applcaton evaluaton plays an mportant role for the enterprse economc benefts, nnovaton ablty and culture. he evaluaton of the enterprse knowledge management applcaton can gude the enterprse knowledge management mplement better. In ths paper, an enterprse knowledge management applcaton evaluaton ndex system s constructed and fuzzy extended analytc herarchy process and cloud gravty center model are employed to evaluate the enterprse knowledge management applcaton problem because t has the advantages of smple, less tme takng and dealng wth qualtatve and quanttatve ndcators smultaneously. An mplementaton of the proposed model s used for a certan enterprse knowledge management applcaton evaluaton, and the results shows the proposed model s feasblty and effectve for the evaluaton. Index erms enterprse knowledge management, fuzzy extended analytc herarchy process, cloud gravty center, evaluaton. I. INRODUCION Knowledge management presents the use of collectve wsdom to mprove adaptablty and nnovaton for enterprses, t provdes a new way to share explct knowledge and tact knowledge n enterprse members. Accordng to mnng and organzng the knowledge of enterprse s ndvduals, the whole obect and the overall benefts of the enterprse can be also enhanced, and the workflow of the enterprse can be ordered clearly. he applcaton of enterprse knowledge management needs to establsh the enterprse's own knowledge base and promote the knowledge exchange between the employees. It need establsh the nternal knowledge envronment of the enterprse, f employees can share nformaton and knowledge n the envronment and contrbute ther wsdom and ablty, t wll be transformed nto knowledge productvty or creatvty, and the generated beneft wll be far superor to the generated beneft by captal, labor, land, machnery and so on. he applcaton of knowledge management n teamwork wll ncrease the effectveness of collectve decson-makng and t wll become the most mportant contrbutor to busness growth. Knowledge wll become the most mportant asset of the enterprse. he role of the applcaton of the knowledge management can be shown as a dynamc expresson of knowledge producton, knowledge sharng, knowledge applcaton and nnovaton of the systematc process, t not only realze the knowledge-sharng, but also ncrease the core competency of the enterprse, at the same tme, t make a great tranng of the enterprse staff sklls and nnovaton capablty, t enhances the response capacty of enterprses, the effcency of enterprse and the nsght of the enterprse [-]. Knowledge management has outstandng contrbutons of the enterprse s core competences, many scholars have formed a consensus on ths pont and do some research n the evaluaton problems of the knowledge management. wana [3] studes the knowledge management performance assessment from the customer perspectve. Andersen [4] present a KA method whch can evaluate knowledge management from leadershp, culture, technology, learnng and assessment fve aspects. Davenport [5] evaluates the knowledge management performance wth the growth of related resources, knowledge content and effcency of knowledge growth, the popularty of knowledge management proects, the concept of knowledge management staff acceptance, the possblty of fnancal recovery of the ndcators. Dekker & Hoon [6] thnks that the value of ntellectual captal s the enterprse knowledge product, and they dvded ntellectual captal nto many ndependent parts, then measure each basc element to assessment the knowledge management. Kaplan [7] creates the Balanced corecard(bc) method, and he assess knowledge management ncludes not only fnancal, but also customers, nternal busness processes, nnovaton, learnng and growth. Base on BC, Edvssnnl [8] present a navgator model, he focus on fve areas ncludng customer, fnancal, process, human factors, renewal and development, whch contans 30 ndcators of an ndex system. Cho &Lee [9] also study the relatonshp between the knowledge management and the enterprse s performance base on BC, they present a evaluaton method from market share, growth rate, proftablty, nnovaton and enterprse scale changes fve aspects. 0 ACADEY PUBLIHER do:0.4304/cp.6.6.0-6
JOURNAL OF COPUER, VOL. 6, NO. 6, JUNE 0 he enterprse knowledge management applcaton evaluaton s an mportant part of the enterprse knowledge management; t s also one of the most mportant problems of the enterprse. However, enterprse knowledge management s a complex system, ncludng management scence, nformaton technology, behavoral scence, cogntve scence and psychology. he applcaton of the enterprse knowledge management relates to organzatonal structure, culture, nsttutonal arrangements and ncentve mechansms of the enterprse. It nvolves lots of factors whch contan quanttatve and qualtatve factors. It has brought great dffcultes of enterprse knowledge management applcaton evaluaton. Currently t s lack of the study of the evaluaton. herefore, t s necessary to research the enterprse knowledge management applcaton evaluaton. In ths paper, we employ the cloud gravty center model and FEAHP to evaluate the enterprse knowledge management applcaton, the cloud gravty center model can deal wth the quanttatve value, fuzzy value and especally the language value, whch are always n knowledge related evaluaton. he FEAHP method wll obtan the ndexes weghts n ths study, because ths method s also use the fuzzy number to compare the mportance of the ndexes, t more easly than usng exact numbers -9 n AHP for the experts. hese characterstc are more sutable for the enterprse knowledge management applcaton evaluaton. For the comparson wth the Fuzzy AHP method, the proposed method can obtan the better performance, and the results shows the proposed model s feasblty and effectve for the evaluaton he remander of the paper s organzed as follows: ecton gves the prncple of the cloud gravty center model. In ecton 3, the FEAHP method s used to determne the weght of the cloud gravty center model. ecton 4 gves a complete mplementaton of a certan enterprse knowledge management applcaton evaluaton. ecton 5 contans the concluson of the paper. II. CLOUD GRAVIY CENER ODEL Cloud theory uses natural language concepts to change the qualtatve concept nto a quanttatve value. It collects the fuzzness and randomness completely. Cloud gravty center model can acheve qualtatve and quanttatve propertes ratonal converson and t can evaluate mult-attrbute ndex better. he basc dea s to use cloud to represent a concept of a qualtatve and quanttatve transformaton model between the uncertanty, the number of the cloud features s expected value Ex, entropy En and devaton D, and the three values consttutes a qualtatve and quanttatve mappng. Ex s the cloud's center of gravty whch expresses the correspondng central value of fuzzy concept. En s a measure of the concept of ambguty and the value reflects the accepted number of elements by vague concept n a doman, whch s a margn. D s a measure of cloud thckness and ts value s the maxmum value of the whole cloud thckness, whch reflects the dsperson of the cloud. When the research queston s pure randomness, En, for the pure ambguty problem, D = 0. he cloud gravty center can be calculated by =a b, n whch, a s the locaton of cloud gravty center, b s the heght of the cloud gravty center. Generally speakng, a s the expectatons whch reflect the correspondng center value of fuzzy concept, and b s a constant value whch s equal to 0.37. If two clouds have the same expectatons, t can be dstngushed the mportance by comparng the heght of the cloud gravty center, the heght of the cloud gravty center reflects the mportance of the cloud. he steps of usng cloud gravty center model to evaluate a problem are shown as follows [0] : tep. Use cloud to express the ndex values. Generally speakng, there are quanttatve ndcators and qualtatve ndcators n an ndex system. he treatment method s extractng a decson matrx composed of n states, then t can be expressed by cloud model by Ex + Ex + + Exn Ex = () n. E n max( EX, EX,, Ex n) mn( EX, EX,, Ex n) =.() 6 In whch, E x s the expected value and E n s the entropy value of ndcator x, E x s the value of ndcator x n the state. mlarly, the value of each language type ndcator can also be expresses as a cloud, when an ndcator has n values, the cloud express method s as follows: Ex En+ ExEn +... + ExnEnn Ex = (3) E + E +... + E n n nn E = E + E + + E (4) n n n nn tep. he representaton of the system states If p ndcators descrbe an evaluaton obect, then a vector can express the system s state base on the cloud model. When the system changes state, the ntegrated cloud-dmensonal shape wll change, and ts gravty has also changed. he gravty of a cloud can be present by the locaton a and the center of gravty heght b. If the cloud gravty s the = (,,, p ).then = a b ( =,,, p). When the system state changes, the focus accordngly changes to = (,,, p ).. tep 3. ake sure the ndcator s weght In ths step, a weght determne method can be used to select the ndcator s weght, n ths study, we wll use FEAHP to determne the weght, the detals are n secton III. tep 4. Use the weght to calculate the changes of the cloud gravty 0 ACADEY PUBLIHER
JOURNAL OF COPUER, VOL. 6, NO. 6, JUNE 0 In an deal state, a cloud gravty assumes 0 0 0 a = ( E x, E x,, E xp),and the heght of the cloud s b= ( b, b,, b p ), whch s equal to the weght vector, then the cloud gravty vector can be shown as = a* b = (,,, P) (5) 0 0 0 0 mlarly, to obtan a state of the cloud gravty vector = (,,, p ),n whch, the locaton of the cloud gravty s gotten by the expectatons, then weghted degree of devaton θ can be used to measure the two knds of state dfferences n the focus of the ntegrated cloud the stuaton. Frstly, the deal state and the state of 0 cloud center gravty vectors should be normalzed, and can be obtaned as 0 0 0 / G = (6) 0 0 ( )/ > And the weghted degree of devaton θ s p θ = ( W* G ) (7) = In whch, W s the weght of the ndcator, and smaller value expresses the less dfference. tep 5. Determne the evaluaton set of the ndcators he language set s commonly selected as (none, worst, worse, very poor, poor, ordnary, good, very good, better, best, excellent, perfect), and the language can change nto a cloud gravty center whch s called cloud generator s shown n Fg. III. FEAHP EHOD Accordng to above ntroducton, a key problem for usng cloud gravty center model s to gve the reasonable weght at step 3. One of the well-known weght determned method s the analytc herarchy process(ahp) method, whch s proposed by aaty [-], AHP s sutable for systematc mult-crteron decson makng problems wth complex herarchy structure, and ths method can deal wth not only the quanttatve factors but also the qualtatve factors. It has the advantage of practcablty, systemc and smplcty. However, t has been generally crtczed because of the use of a dscrete scale of one to nne whch cannot handle the uncertanty and ambguty present n decdng the prortes of dfferent attrbutes, but the appearance of FEAHP [3-4] has provded a soluton to ths problem, because decson-makers usually fnd that t s more confdent to gve a fuzzy number than a precse number udgments. And the method has been appled successfully n many felds [5-9]. herefore, We employed the FEAHP method to determne the weght of cloud gravty center model. rangular fuzzy numbers (FN) are used n FEAHP, and a typcal FN s graphcally depcted n Fg.. If a, b and c, respectvely denote the smallest possble value, the most promsng value and the largest possble value that descrbe a fuzzy event, then the FN can be denoted as a trplet (a,b,c). Generally, a b c, And the relatonshp between x whch can be any pont between a, b and c, and ts membershp y conforms to [ ] [ ] x = a+ ( b a) y x a, b x = c + ( b c) y x b, c 0 otherwse hen the one to nne scale used n decdng the prortes of dfferent attrbutes n tradtonal AHP can be substtuted by FN accordng to able. And the weght can be calculated by the four steps as follows: (where all the g are FNs whose parameters are a, b, and c) (8) Fgure.. A cloud generator whch can change the language value nto a cloud gravty value 0 ACADEY PUBLIHER
JOURNAL OF COPUER, VOL. 6, NO. 6, JUNE 0 3 ABLE II FUZZY CONVERION CALE Lngustc scale FN scale FN recprocal scale Just equal (,,) (,,) Equally mportant (/,,3/) (/3,,) Weakly mportant (,3/,) (/,/3,) trongly more mportant (3/,,5/) (/5,/,/3) Very strongly more mportant (,5/,3) (/3,/5,/) Absolutely more mportant (5/,3,7/) (/7,/3,/5) Fgure. A trangular fuzzy number And the weght can be calculated by the four steps as follows: (where all the are FNs whose parameters are a, b, and c) g tep. he value of fuzzy synthetc extent wth respect to the th obect s defned as: o obtan m n m = g g = = = m = (9) g, perform the fuzzy addton operaton of m extent analyss values for a partcular matrx such that m m m m g = a,,,,,, b c = m (0) = = = = n m and to obtan g,perform the fuzzy addton = = operaton of (=,,,m)values such that n m = = g g,,,,,, = = n () n n n c b a = = = tep.he degree of possblty of s defned as: V = hgt( ) = µ ( d) b b () = 0 a c a c otherwse ( b c) ( b a) V ( ) = hgt( ) = µ ( d) b b (3) = 0 a c a c otherwse ( b c) ( b a) Where d s the ordnate of the hghest ntersecton pont d between µ and µ. tep 3. he degree of possblty for a convex fuzzy number to be greater than k convex fuzzy numbers (=,,,k) can be defned as follows: (,,, ) = mn ( ) V V (4) tep 4. Assume that d ( A ) = V ( ) the weght vector s gven by k. hen k (,, ) W = d A d A d A n (5) where A ( =,,, n) are n elements. hen normalze the vector to get the normalzed weght vectors whch are W ( d( A), d( A), d( A n )) non-fuzzy number. = where W s a 0 ACADEY PUBLIHER
4 JOURNAL OF COPUER, VOL. 6, NO. 6, JUNE 0 IV. A NUERIC EXAPLE An example of enterprse knowledge management evoluton s presented n ths secton. Frstly, a three levels herarchy structure enterprse knowledge management evoluton ndex system s created. he frst level s the obect level, and t need to dvde some sub ndexes, wth the survey of some experts, the second level contans three factors whch are:. he economc revenue of knowledge applcaton Obvously, the enterprse managers are also pay attenton to the enterprse s economc revenue. Only transferrng to economc revenue can make the enterprse manager focus on the knowledge applcaton.. he workers qualty ncreasng of knowledge applcaton Knowledge applcaton transferrng by each workers can make workers workng effectually. When a enterprse forms a culture, the whole workers capacty would ncrease rapdly. 3. Knowledge management equpment Good knowledge management equpment s helpful of the knowledge applcaton. Nowadays, the most mportant equpment s knowledge nformaton system, and the maor part s the knowledge database. he three factors can also be dvded nto several ndexes, and the whole evaluaton system s shown n able ABLE I. HE ENERPRIE KNOWLEDGE ANAGEEN APPLICAION EVALUAION INDEX YE Level Level Level 3 he revenue ncreasng rate of knowledge applcaton(c) he economc revenue of knowledge applcaton(b) he technque level ncreasng of knowledge applcaton(c) Enterprse knowledge management applcaton he workers qualty ncreasng of knowledge applcaton(b) Knowledge management equpment(b3) he contrbuton value of knowledge applcaton(c3) Cultural qualty(c4) Ablty to dentfy and nternalzaton of knowledge(c5) he workers capacty ncreasng through knowledge(c6) Knowledge database level(c7) Knowledge nformaton system level(c8) Knowledge database update level(c9) ABLE III. HE CORE OF HE INDICAOR OF AN ENERPRIE ndex C C C3 C4 C5 C6 C7 C8 C9 state 90 5 good good 4 76 4 4 3 state 86 4 very good very good 5 74 4 4 3 state 3 84 5 very good good 4 7 4 4 4 deal state 00 5 perfect perfect 5 00 5 5 5 ABLE IV. EXPECED VALUE E X AND ENROPY E N OF EACH INDICAOR ndex C C C3 C4 C5 C6 C7 C8 C9 E x 86.7 4.7 0.67 0.63 4.33 74 4 4 3.33 E n 0. 0.07 0.07 0.7 0.67 0 0 0.7 ABLE IV. HE FUZZY EVALUAION OF C,C,C 3 C C C 3 W C (,,) (/5,/,/3) (,3/,) 0.335 C (3/,,5/) (,,) (,3/,) 0.397 C 3 (/,/3,) (/,/3,) (,,) 0.68 0 ACADEY PUBLIHER
JOURNAL OF COPUER, VOL. 6, NO. 6, JUNE 0 5 ABLE V. HE FUZZY EVALUAION OF LEVEL WO AND I FINAL WEIGH C C C 3 W C (,,) (/,,3/) (/,/3,) 0.4 C (/3,,) (,,) (/,/3,) 0.30 C 3 (,3/,) (,3/,) (,,) 0.8 ABLE VI. HE FUZZY EVALUAION OF LEVEL WO AND I FINAL WEIGH C C C 3 W 3 C (,,) (3/,,5/) (3/,,5/) 0.4 C (/5,/,/3) (,,) (/,,3/) 0.30 C 3 (/5,/,/3) (/3,,) (,,) 0.9 Based on the above knowledge management applcaton comprehensve evaluaton ndex system, Cloud gravty center model and FEAHP method, an enterprse knowledge management applcaton evaluaton can be evaluated as follows, select three tme ponts of the enterprse s state and use the experts score method, the score of the ndcators can be shown n able 3. Accordng Fg. the language can be transferred as the quanttatve values, and the decson matrx s gotten as 90 5 0.6 0.6 4 76 4 4 3 86 4 0.7 0.7 5 74 4 4 3 84 5 0.7 0.6 4 7 4 4 4 he deal state vector s a 0 =(00,5,,,5,00,5,5,5). Accordng to equaton -4, expected value E x and entropy E n can be calculated, and the results s shown n table 4. Usng FEAHP to calculate the weght of the ndcators. At frst, the fuzzy evaluaton matrx of the second level s constructed by the parwse comparson of the dfferent ndcators usng trangular fuzzy numbers, whch s shown n able 4-6. he dfferent values of fuzzy synthetc extent wth the three ndcators are denoted by,, 3 respectvely, Accordng to Eq.() the weght of the three ndcators(w ) can be calculated as follows: =.4, 3, 3.67 (/.7,/ 9.83,/ 7.9) = (0.97, 0.305, 0.464) = 3.5, 4.5, 5.5 (/.7,/ 9.83,/ 7.9) = (0.88, 0.458, 0.696) 3 =,.33, 3 (/.7,/ 9.83,/ 7.9) = (0.64, 0.37, 0.379) Usng Eq.(4) to compare wth,, 3, V ( ) = 0.536 ; V ( 3) = ; V ( 3) = ; V ( 3 ) = 0.79, V ( 3 ) = 0.95. hen (, 3, 4).536 ; d = V = d 3 =.04. d = 3 ; he weght s W = ( 0.335,0.397,0.68) W = ( 0.4,0.30,0.8) and.mlarly, W 3 = 0.9,0.30,0.4 can be obtaned, and the fnal weght vector s w = ( 0.6,0.,0.4,0.3,0.09,0.08,0.08,0.08,0.). And the heght of the cloud gravty center vector b s equal to w b = ( 0.6,0.,0.4,0.3,0.09,0.08,0.08,0.08,0.) accordng equaton 5, the deal cloud gravty vector s a = ( 6.34,0.57,0.4,0.3,0.46,8.6,0.4,0.4,0.57) and the enterprse cloud gravty vector s a = ( 4.7,0.53,0.0,0.08,0.40,6.,0.33,0.34,0.38). Accordng to equaton 6 and equaton 7, the weghted degree of devaton θ s 0.7. In cloud generator, t wll actvate the "good" and "very good" two propertes, but more emphass on "good" state. herefore, the enterprse knowledge management applcaton result s good. In order to prove the proposed method, we employ several experts for scorng the state of the enterprse s knowledge management applcaton, each ndexes score s between 0 and 00. And the average value s used as the fnal score of the enterprse s knowledge management applcaton, the average s s = ( 86.67,93.33,66.67,66.67,86.67,74,80,80,67.67), and the weght s used the same weght w whch s gotten by FEAHP, the fnal score s 75.99 obtaned by wb. It express state of the enterprse s knowledge applcaton s between "good" and "very good" state. However, t more emphass on " very good " state. For comparng the two method, t s easly seen that two method are also evaluate the state of the enterprse s knowledge applcaton s between "good" and "very good" state. he dfference s the proposed method s emphass good sde, and the other method emphass the other sde. he reason s that the proposed method can more easly deal wth the dfferent values of dfferent attrbute, especally the language values. When we use the score nstead the language values, t would have some devaton as the other method. 0 ACADEY PUBLIHER
6 JOURNAL OF COPUER, VOL. 6, NO. 6, JUNE 0 V. CONCLUION Knowledge management applcaton can brng to the enterprse contnuously upgrade the comprehensve performance, and t also brng to the enterprses overall compettveness. Knowledge management applcaton can mprove the economc effcency, staff qualty, nnovaton and technology ablty of the enterprse. It wll make the busness more stable by the postve effects. In ths study, a cloud gravty model and a fuzzy extended AHP (FEAHP) approach has been presented to evaluate an enterprse knowledge management applcaton. A three levels herarchy ndex system are also presented, whch s evaluatng from the economc revenue of knowledge applcaton, the workers qualty ncreasng of knowledge applcaton and knowledge management equpment three aspects, and the results obtaned n the example reflect the stuaton of the enterprse knowledge management applcaton. It also can be used for any other enterprse. case of awanese stone ndustry, European Journal of Operatonal Research,(007), 76 795-80 [6] Chng-Hsue C, Evaluatng naval tactcal mssle systems by fuzzy AHP based on the grade value of membershp functon European Journal of Operatonal Research 997;96():343-350. [7] Weck, Klocke F, chell H, Ruenauver E, Evaluatng alternatve producton cycles usng the extended fuzzy AHP method, European Journal of Operatonal Research, 997;00():35-366. [8] Cheng CH, Yang KL, Hwang CL, Evaluatng attack helcopters by AHP based on lngustc varable weght, European Journal of Operatonal Research, 999;6():43-443. [9] F.unc Bozbura, Ahmet Beskese,Cengz Kahraman. Prortzaton of human captal measurement ndcators usng fuzzy AHP; Expert ystems wth Applcatons, 007:3:00-. Jngl Zheng was born n Chna n 976, and work n Chongqng Normal unversty n Chongqng n Chna, and s studyng for a doctorate n Chongqng Unversty. Her research nterests s knowledge management theory and applcaton. REFERENCE [] Pllana. Demystfyng knowledge management [J]. Busness trategy eres,009,(0) [] ngers,j. anagement knowledge and knowledge management:realsm and forms of truth[j]. Knowledge anagement Research & Practce,008,(6) [3] wana, A., he Essental Gude o Knowledge anagement: E-Busness and Crm Applcatons. Atlanta :Prentce-Hall,00. [4] Andersen, A., he Knowledge anagement Assessment ool (KA).London: Arthur Andersen Kmat tudy,996. [5] Davenport,. H.,&Lawrence P., Workng Knowledge: How Organzatons anage What hey Know, Harvard Busness chool Press, 997. [6] Dekker, R.,&De Hoog, R., he onetary Value of Knowledge Assets: A cro Approach. Expert ystems wth Applcatons, 000,3(8):-4. [7] Kaplan, R..,&Norton, D. F., he Balanced corecard easures hat Drve Performance. Harvard Busness Revew, 99, 70 ():7-80. [8] Edvnsson L,&ullvan P., Developng A odel For anagng Intellectual Captal, European anagement Journal, 996, 4 (4): 356-364. [9] Cho B.&Lee H., Knowledge anagement trategy and Its Lnk o Knowledge Creaton Process, Expert ystems Wth Applcatons. 00, (3): 73-87. [0] LIAO Lang-ca; FAN Ln-un; WANG Peng, ethod of Evaluatng Organzatonal Performance Based on Cloud heory, ystems Engneerng,00,9(7):56-58. [] aaty,.l. he analytc herarchy process, 980. New York: cgraw-hll Book Co [] aaty,.l. Fundamentals of decson makng and prorty theory wth the AHP, 994. Pttsburgh: RW Publcatons [3] Chang, D.Y, Extent analyss and synthetc decson, optmzaton technques and applcatons. ngapore: World centfc,99 [4] Chang, D.Y, Applcatons of the extent analyss method on fuzzy AHP, European Journal of Operatonal Research, 996,95, 649-655. [5] Vctor B. Kreng, Chao-Y Wu, Evaluaton of knowledge portal development tools usng a fuzzy AHP approach: he 0 ACADEY PUBLIHER